ACCORDION: Clustering and Selecting Relevant Data for Guided Network Extension and Query Answering.

Querying new information from knowledge sources, in general, and published literature, in particular, aims to provide precise and quick answers to questions raised about a system under study. In this paper, we present ACCORDION (Automated Clustering Conditional On Relating Data of Interactions tO a Network), a novel tool and a methodology to enable efficient answering of biological questions by automatically assembling new or expanding existing models using published literature. Our approach integrates information extraction and clustering with simulation and formal analysis to allow for automated iterative process that includes assembling, testing and selecting most relevant models, given a set of desired system properties. We demonstrate our methodology on a model of the circuitry that controls T cell differentiation. To evaluate our approach, we compare the model that we obtained, using our automated model extension approach, with the previously published manually extended T cell model. Besides being able to automatically and rapidly reconstruct the manually extended model, ACCORDION can provide multiple viable extended model versions. As such, it replaces large number of tedious or even impractical manual experiments and guides alternative interventions in real biological systems.